Sustained casing pressure (SCP) is a primary indicator of well integrity degradation, arising from compromised barriers such as cement, casing, tubing/packer components, or wellhead seals. Traditional SCP diagnostics—based on manual interpretation of annular pressure trends, bleed-off tests, and operational records—are often time-consuming, analyst-dependent, and difficult to scale across large well populations. This study presents a physics-informed machine learning (PIML) framework that integrates engineering-based physical principles, including fluid compressibility, thermal expansion, and leak-path mechanics, into a machine learning workflow for automated and scalable SCP classification. The framework is applied to field monitoring data from 26 wells, with detailed multi-annulus case studies for wells A5, A8, and A12. The method classifies cycle-level SCP behavior into six diagnostic types—no pressure, thermal pressure, trapped pressure, recharge pressure, constant pressure, and high-rate recharge—and maintains physically plausible and interpretable outputs under noisy or complex pressure signatures. Operationally, the framework supports real-time, SCADA-integrated surveillance to enable early detection of integrity threats, prioritization of higher-risk wells, and proactive intervention planning. The novelty of this work lies in embedding physical constraints directly into the classification logic, producing a robust and interpretable diagnostic capability that advances SCP analysis from a reactive, manual task toward automated well-integrity surveillance applicable to offshore, HPHT, CO₂ sequestration, and hydrogen storage operations.

Anthropogenic emissions reached 37.4 Gt/a in 2023, intensifying the need for effective carbon storage in subsurface formations to mitigate global warming. Carbon Capture and Storage (CCS) has emerged as a viable solution, with over 43 operational sites worldwide and projections for more than 840 projects by 2040, potentially storing 2225 Mt CO₂ annually. This review provides a comprehensive analysis of CCS technologies, focusing on the integrity, safety, and economic viability of storage sites, which are crucial for long-term success. It identifies knowledge gaps in existing research, revealing that most studies address specific aspects of CCS but lack integrated approaches combining data, technologies, risks, and economic assessments. Some studies emphasize numerical modeling and fault reactivation risks but overlook issues such as cement degradation and casing corrosion, which are critical to preventing wellbore leakage. Others explore CO₂-rock interactions without considering cement integrity or focus on cement degradation without accounting for other field-scale risks. This review bridges these gaps by examining failures across wellbores, reservoirs, and caprocks, including cement integrity, casing corrosion, uplifting, fault activation, and seismicity due to injection. It also covers numerical modeling, experimental work, and monitoring techniques to ensure CCS integrity. Additionally, this review assesses economic risks to build confidence in CCS deployment, offering a comprehensive framework to ensure secure and long-term CO₂ storage in subsurface formations.